Project Summary This project proposes further development of a computational discovery platform shown to quantifiably identify and prioritize both targets and compounds for therapeutic intervention, which will accelerate the development of newer, more effective drugs with fewer side-effects. This platform is based on a novel fusion of two state of the art computational approaches (phenolog mapping and functional networks) with proprietary data from Genetic Networks powerful gene-drug screening assays (H-Tech and Y-Tech) that identify drug targets, conserved drug target pathways, and off-target effects (e.g. toxicity) by genome- wide phenotypic profiling. The computational platform identifies conserved and functionally linked genes to define conserved biological modules, groups of gene that work tightly together and contribute to disease phenotypes and drug responses. The first aim is to improve the throughput and robustness of Genetic Networks computational platform to provide the pharmaceutical industry with a proven tool to select better compounds for their drug development pipelines and clinical trials. The second aim is to improve the prioritization of target genes that will be most effective in treating disease. Many genes have very similar copies, known as paralogs, which can complicate the interpretation of biological data. This project will integrate diverse biological information to prioritize the best gene target based on the disease, the relevant tissue, and how each gene interacts with other genes. The third aim is to identify common groups of genes involved in multiple disease and/or multiple drug responses. Understanding the genes involved in multiple biological processes allows pharmaceutical companies to repurpose already approved drug for new purposes and lowers the cost of developing multiple drugs for multiple disease. In addition, this approach will identify the potential drug interactions that can lead to severe side-effects when drugs are taken together without requiring animal testing or risking patient lives. This automated system will rapidly identify and prioritize therapeutic interventions across multiple diseases and will increase the success rate of drug discovery and provide guidance to repurpose existing drugs for new indications. Implementation of the platform described in this proposal will strengthen Genetic Networks' contributions to the goal of bringing new treatments to patients faster.